The Internet of Things (IoT) is constituted of devices that are exponentially growing in number and in complexity. They use numerous customized firmware and hardware, without taking into consideration security issues, which make them a target for cybercriminals, especially malware authors.We will present a novel approach of using side channel information to identify the kinds of threats that are targeting the device. Using our approach, a malware analyst is able to obtain precise knowledge about malware type and identity, even in the presence of obfuscation techniques which may prevent static or symbolic binary analysis. We recorded 100,000 measurement traces from an IoT device infected by various in-the-wild malware samples and realistic benign activity. Our method does not require any modification on the target device. Thus, it can be deployed independently from the resources available without any overhead. Moreover, our approach has the advantage that it can hardly be detected and evaded by the malware authors. In our experiments, we were able to predict three generic malware types (and one benign class) with an accuracy of 99.82%. Even more, our results show that we are able to classify altered malware samples with unseen obfuscation techniques during the training phase, and to determine what kind of obfuscations were applied to the binary, which makes our approach particularly useful for malware analysts. CCS CONCEPTS• Security and privacy → Malware and its mitigation.
Modeling and simulation play an important role in transportation networks analysis. With the widespread of personalized real-time information sources, it is relevant for the simulation model to be individual-centered. The agent-based simulation is the most promising paradigm in this context. However, representing the movements of realistic numbers of travelers within reasonable execution times requires significant computational resources. It also requires relevant methods, architectures and algorithms that respect the characteristics of transportation networks. In this paper, we tackle the problem of using high-performance computing for agent-based traffic simulations. To do so, we define two generic agent-based simulation models, representing the existing sequential agent-based traffic simulations. The first model is macroscopic, in which travelers do not interact directly and use a fundamental diagram of traffic flow to continuously compute their speeds. The second model is microscopic, in which travelers interact with their neighbors to adapt their speeds to their surrounding environment. We define patterns to distribute these simulations in a high-performance environment. The first distributes agents equally between available computation units. The second pattern splits the environment over the different units. We finally propose a diffusive method to dynamically balance the load between units during execution. The results show that agent-based distribution is more efficient with macroscopic simulations, with a speedup of 6 compared to the sequential version, while environmentbased distribution is more efficient with microscopic simulations, with a speedup of 14. Our diffusive load-balancing algorithm improves further the performance of the environment based approach by 150%.
With the generalization of real-time traveler information, the behavior of modern transport networks becomes harder to analyze and to predict. It is now critical to develop simulation tools for mobility policies makers, taking into account this new information environment. Information is now individualized, and the interaction of a huge population of individually guided travelers have to be taken into account in the simulations. However, existing mobility multiagent and micro-simulations can only consider a sample of the real volumes of travelers, especially for big regions. With distributed simulations, it would be easier to analyze and predict the status of nowadays and future networks, with informed and connected travelers. In this paper, we propose a comparison between two methods for distributing multiagent travelers mobility simulations, allowing for the consideration of realistic travelers flows and wide geographical regions
Modeling and simulation play an important role in transportation networks analysis. With the widespread use of personalized real-time information sources, the behavior of the simulation depends heavily on individual travelers reactions to the received information. As a consequence, it is relevant for the simulation model to be individual-centered, and multiagent simulation is the most promising paradigm in this context. However, representing the movements of realistic numbers of travelers within reasonable execution times requires significant computational resources. It also requires relevant methods, architectures and algorithms that respect the characteristics of transportation networks. In this paper, we define two multiagent simulation models representing the existing sequential multiagent traffic simulations. The first model is fundamental diagram-based model, in which travelers do not interact directly and use a fundamental diagram of traffic flow to continuously compute their speeds. The second model is car-following based, in which travelers interact with their neighbors to adapt their speeds to their surrounding environment. Then we define patterns to distribute these simulations in a high-performance environment. The first is agent-based and distributes agents equally between available computation units. The second pattern is environment-based and partitions the environment over the different units. The results show that agent-based distribution is more efficient with fundamental diagram-based model simulations while environment-based distribution is more efficient with car following-based simulations.
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